New and evolving threats emerge every day in the e-Health industry. The safety of e-Health’s telemonitoring systems is becoming a prominent task. In this work, starting from a CADS (Cyberattack Detection System) model that uses artificial intelligence techniques to detect anomalies, we focus on the activity of interacting with data. Using a User Interaction Engine, a dashboard allows you to visually explore and view data from suspected attacks on healthcare professionals for a threat reaction. In particular, a User Feedback module is presented to interact with healthcare personnel and ask for a response on the anomaly detected.
User Feedback to Improve the Performance of a Cyberattack Detection Artificial Intelligence System in the e-Health Domain / Ardito, C.; Di Noia, T.; Di Sciascio, E.; Lofù, D.; Pazienza, A.; Vitulano, F.. - 12936:(2021), pp. 295-299. (Intervento presentato al convegno 18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021 nel 2021) [10.1007/978-3-030-85607-6_25].
User Feedback to Improve the Performance of a Cyberattack Detection Artificial Intelligence System in the e-Health Domain
Ardito C.;Di Noia T.;Di Sciascio E.;Lofù D.
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2021-01-01
Abstract
New and evolving threats emerge every day in the e-Health industry. The safety of e-Health’s telemonitoring systems is becoming a prominent task. In this work, starting from a CADS (Cyberattack Detection System) model that uses artificial intelligence techniques to detect anomalies, we focus on the activity of interacting with data. Using a User Interaction Engine, a dashboard allows you to visually explore and view data from suspected attacks on healthcare professionals for a threat reaction. In particular, a User Feedback module is presented to interact with healthcare personnel and ask for a response on the anomaly detected.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
